Business Intelligence (BI) generally refers to software tools used to improve business enterprise decision-making. These tools are commonly applied to financial, human resource, marketing, sales, customer and supplier analyses. More specifically, these tools can include: reporting and analysis tools to present information, content delivery infrastructure systems for delivery and management of reports and analytics, data warehousing systems for cleansing and consolidating information from disparate sources, and data management systems to collect, store, and manage rawxAdata.
On-line Analytical Processing (OLAP) tools are a subset of business intelligence tools. There are a number of commercially available OLAP tools including Business Objects Voyager™ which is available from Business Objects, an SAP Company, San Jose, Calif. OLAP tools generate reports and are otherwise suited for ad hoc analyses. OLAP generally refers to a technique of providing fast analysis of shared multi-dimensional information stored in a database. OLAP systems provide a multi-dimensional conceptual view of data, including full support for hierarchies and multiple hierarchies. This framework is used because it is a logical way to analyze business information. In some OLAP tools the data is arranged in a schema that simulates a multidimensional schema. The multi-dimensional schema means redundant information is stored, but it allows for users to initiate queries without the need to know how the data is organized.
The size of a multi-dimensional data source grows geometrically with the number of dimensions that characterize the data. However, the number of populated members in the data grows at a slow rate. This leads to the data source being sparse. Typically, a sparse data source has non-populated values (e.g. null or zero values) in ninety percent or more of its cells.
Operations to retrieve or manipulate data in a sparse data source can be very inefficient because the operations often need to visit each and every member in a specified range of dimensions or combinations of dimensions. Typically these members are visited whether populated or not. Thus, in sparse cubes all members of the cube are typically visited, yet the majority of the members have no effect on the results.
In view of the foregoing, it would be desirable to provide improved techniques for processing sparse multi-dimensional data.